Deep Belief Network Training Improvement Using Elite Samples Minimizing Free Energy
This is an incremental improvement for researchers and practitioners using DBNs in machine learning.
The paper tackles the problem of improving Deep Belief Network (DBN) training by introducing a method that uses free energy to select elite samples for more accurate gradient computation, resulting in error rates of 0.99% on MNIST and 3.59% on ISOLET, outperforming prior methods.
Nowadays this is very popular to use deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. In this paper we present an improvement in a common method that is usually used in training of RBMs. The new method uses free energy as a criterion to obtain elite samples from generative model. We argue that these samples can more accurately compute gradient of log probability of training data. According to the results, an error rate of 0.99% was achieved on MNIST test set. This result shows that the proposed method outperforms the method presented in the first paper introducing DBN (1.25% error rate) and general classification methods such as SVM (1.4% error rate) and KNN (with 1.6% error rate). In another test using ISOLET dataset, letter classification error dropped to 3.59% compared to 5.59% error rate achieved in those papers using this dataset. The implemented method is available online at "http://ceit.aut.ac.ir/~keyvanrad/DeeBNet Toolbox.html".